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Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks
[chapter]
2018
Lecture Notes in Computer Science
We propose a neural approach for fusing an arbitrary-length burst of photographs suffering from severe camera shake and noise into a sharp and noise-free image. Our novel convolutional architecture has a simultaneous view of all frames in the burst, and by construction treats them in an order-independent manner. This enables it to effectively detect and leverage subtle cues scattered across different frames, while ensuring that each frame gets a full and equal consideration regardless of its
doi:10.1007/978-3-030-01237-3_45
fatcat:e4l2tmdgtzfb3fj7rujcnrduoi